Performance Analysis of Neural Network-Based Uncalibrated Hand-Eye Coordination

Su, Jianbo

doi:10.1007/11427469_35

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2009

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“…Performance of this kind of systems has also been investigated in ref. [13]. However, since the off-line training of the ANN must involve a sufficiently large number of training data, feasibility of this approach is restricted in practice.…”

Section: Introductionmentioning

confidence: 99%

Convergence analysis for the uncalibrated robotic hand–eye coordination based on the unmodeled dynamics observer

2009

Robotica

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SUMMARYThe uncalibrated robotic hand–eye coordination problem is firstly modeled by a dynamic system, where the unknown hand–eye relationship is regarded as the system's unmodeled dynamics. A state observer is then designed to estimate impacts of this modeling error together with the system's external disturbances. With the estimation results as the compensation, the system control is thus accomplished based on a nonlinear combination of the system state errors. Convergence analysis of the whole system under the proposed control scheme is emphasized. Simulations and experiment results are presented to verify the performance of the proposed approach.

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Section: Introductionmentioning

confidence: 99%

Performance Analysis of Neural Network-Based Uncalibrated Hand-Eye Coordination

Cited by 1 publication

References 9 publications

Convergence analysis for the uncalibrated robotic hand–eye coordination based on the unmodeled dynamics observer

Convergence analysis for the uncalibrated robotic hand–eye coordination based on the unmodeled dynamics observer

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